1. Technical Field
The present disclosure relates to detection of three-dimensional structures from two-dimensional images and particularly related to application in driver assistance systems
2. Description of Related Art
Structure-from-Motion (SfM) refers to methods for recovering three-dimensional information of a scene that has been projected onto the back focal plane of a camera. The structural information derived from a SfM algorithm may take the form of a set of projection matrices, one projection matrix per image frame, representing the relationship between a specific two-dimensional point in the image plane and its corresponding three-dimensional point. SfM algorithms rely on tracking specific image features from image frame to image frame to determine structural information concerning the scene.
Similarly, stereo or multi-ocular disparity analysis may be used to determine three-dimensional points from two-dimensional images. Finding structure from motion presents a similar problem as finding structure from stereo vision. In both instances, the correspondence between images and the reconstruction of three-dimensional object is found.
In three-dimensional computer graphics, a depth map is an image that contains information relating to the distance Z of surfaces of objects from a viewpoint; the viewpoint generally being the position of a camera.
The computation of depth maps from multiple images, either from a motion sequence and/or from multiple cameras is the subject of extensive research and numerous systems have been demonstrated. These systems are capable of producing dense depth map information but at considerable computational expense. Depth maps may also lack the full spatial resolution of the original gray scale images.
In multi-camera stereo, the epipolar geometry of the cameras is known and dense correspondences are computed by performing a search along the epipolar lines. Structure from Motion (SfM) techniques typically precede the dense epipolar search by computing the camera motion and epipolar geometry. In both cases, a smoothness function is explicitly or implicitly assumed so as to regularize the search and give robustness to noise and brightness changes. In the most straightforward approach, the search is performed by matching images patches rather than individual pixels.
The depth error Ez in multi-camera stereo is a function of forward distance Z to the cameras, the correspondence error (Ed), image resolution (or focal length f in pixels) and the baseline b:
                              E          z                =                                            Z              2                        ⁢                          E              d                                            f            ⁢                                                  ⁢            b                                              (        1        )            
In SfM depth error is similar, however, the baseline b is replaced by the motion of the camera. Since the correspondence error (Ed) is finite, at best on the order of 0.25 pixels in optimal conditions, it may be considered to compute the depth map using data from the highest available image resolution. However, due to the computational cost, it is often inhibitive to compute a depth map at such a large resolution.
Thus there is a need for and it would be advantageous to have a driver assistance system and corresponding method adapted to detect three dimensional structures such as guardrails, curbs or other three dimensional objects in the road environment from depth map images while avoiding intensive computational overhead normally associated with computing depth maps to allow for real time processing in the driver assistance system.